Hyperspectral Imaging for Food-Fiber Components and Fiber-Rich Food-Processing By-Products: Current Evidence, Analytical Challenges, and Valorization Prospects

Food processing by-products such as peels, pomaces, bran, husks, and pulp residues are potential sources of dietary fiber and structural polysaccharides, but their utilization is complicated by variability in composition, moisture, particle size, processing history, and spatial distribution. Hyperspectral imaging technology can capture spectral and spatial information simultaneously and may complement conventional bulk analyses by revealing compositional gradients, particle-level variation, and non-uniform ingredient distribution. This critical review addresses a gap not covered by existing general reviews by synthesizing applications to food fibers, fiber-rich foods, and food-processing by-products, while using selected non-imaging near-infrared spectroscopy studies as supporting evidence. Direct imaging evidence is concentrated in pectin-related prediction and mapping, whereas soluble/insoluble dietary fiber assessment and visualization of added-fiber distribution are represented by isolated proof-of-concept studies. Emerging work also indicates sensitivity to cellulose structure and lignocellulosic organization. Supporting spectroscopy studies demonstrate feasibility for estimating cellulose, hemicellulose, lignin, pectin, and crude fiber, but most rely on dried, ground, or otherwise standardized samples and cannot be transferred directly to imaging systems. Major limitations include prepared calibration mixtures, matrix-specific datasets, limited external validation, insufficient testing across seasons, suppliers, and instruments, and scarce process-integrated studies. Future progress requires representative industrial samples, harmonized reference methods, grouped and external validation, calibration transfer, uncertainty reporting, and stronger links with techno-economic and environmental outcomes. Hyperspectral imaging is most valuable when spatial heterogeneity affects processing or product quality, whereas non-imaging spectroscopy may remain preferable when average composition is sufficient.